It’s NBER Summer Institute season, when every bar and restaurant in East Cambridge, from Helmand to Lord Hobo, is filled with our tribe. The air hums with discussions of Lagrangians and HANKs and robust estimators. And the number of great papers presented, discussed, or otherwise floating around inspires.

The paper we’re discussing today, by Wyatt Brooks at Notre Dame and Kevin Donovan at Yale SOM, uses a great combination of dynamic general equilibrium theory and a totally insane quasi-randomized experiment to help answer an old question: how beneficial is it for villages to be connected to the broader economy? The fundamental insight requires two ideas that are second nature for economists, but are incredibly controversial outside our profession.

First, going back to Nobel winner Arthur Lewis if not much earlier, economists have argued that “structural transformation”, the shift out of low-productivity agriculture to urban areas and non-ag sectors, is fundamental to economic growth. Recent work by Hicks et al is a bit more measured – the individuals who benefit from leaving agriculture generally already have, so Lenin-type forced industrialization is a bad idea! – but nonetheless barriers to that movement are still harmful to growth, even when those barriers are largely cultural as in the forthcoming JPE by Melanie Morton and the well-named Gharad Bryan. What’s so bad about the ag sector? In the developing world, it tends to be small-plot, quite-inefficient, staple-crop production, unlike the growth-generating positive-externality-filled, increasing-returns-type sectors (on this point, Romer 1990). There are zero examples of countries becoming rich without their labor force shifting dramatically out of agriculture. The intuition of many in the public, that Gandhi was right about the village economy and that structural transformation just means dreadful slums, is the intuition of people who lack respect for individual agency. The slums may be bad, but look how they fill up everywhere they exist! Ergo, how bad must the alternative be?

The second related misunderstanding of the public is that credit is unimportant. For folks near subsistence, the danger of economic shocks pushing you near that dangerous cutpoint is so fundamental that it leads to all sorts of otherwise odd behavior. Consider the response of my ancestors (and presumably the author of today’s paper’s ancestors, given that he is a Prof. Donovan) when potato blight hit. Potatoes are an input to growing more potatoes tomorrow, but near subsistence, you have no choice but to eat your “savings” away after bad shocks. This obviously causes problems in the future, prolonging the famine. But even worse, to avoid getting in a situation where you eat all your savings, you save more and invest less than you otherwise would. Empirically, Karlan et al QJE 2014 show large demand for savings instruments in Ghana, and Cynthia Kinnan shows why insurance markets in the developing world are incomplete despite large welfare gains. Indeed, many countries, including India, make it illegal to insure oneself against certain types of negative shocks, as Mobarak and Rosenzweig show. The need to save for low probability, really negative, shocks may even lead people to invest in assets with highly negative annual returns; on this, see the wonderfully-titled Continued Existence of Cows Disproves Central Tenets of Capitalism? This is all to say: the rise of credit and insurance markets unlocks much more productive activity, especially in the developing world, and it is not merely the den of exploitative lenders.

Ok, so insurance against bad shocks matters, and getting out of low-productivity agriculture may matter as well. Let’s imagine you live in a tiny village which is often separated from bigger towns, geographically. What would happen if you somehow lowered the cost of reaching those towns? Well, we’d expect goods-trade to radically change – see the earlier post on Dave Donaldson’s work, or the nice paper on Brazilian roads by Morten and Oliveria. But the benefits of reducing isolation go well beyond just getting better prices for goods.

Why? In the developing world, most people have multiple jobs. They farm during the season, work in the market on occasion, do construction, work as a migrant, and so on. Imagine that in the village, most jobs are just farmwork, and outside, there is always the change for day work at a fixed wage. In autarky, I just work on the farm, perhaps my own. I need to keep a bunch of savings because sometimes farms get a bunch of bad shocks: a fire burns my crops, or an elephant stomps on them. Running out of savings risks death, and there is no crop insurance, so I save precautionarily. Saving means I don’t have as much to spend on fertilizer or pesticide, so my yields are lower.

If I can access the outside world, then when my farm gets bad shocks and my savings runs low, I leave the village and take day work to build them back up. Since I know I will have that option, I don’t need to save as much, and hence I can buy more fertilizer. Now, the wage for farmers in the village (including the implicit wage that would keep me on my own farm) needs to be higher since some of these ex-farmers will go work in town, shifting village labor supply left. This higher wage pushes the amount of fertilizer I will buy down, since high wages reduce the marginal productivity of farm improvements. Whether fertilizer use goes up or down is therefore an empirical question, but at least we can say that those who use more fertilizer, those who react more to bad shocks by working outside the village, and those whose savings drops the most should be the same farmers. Either way, the village winds up richer both for the direct reason of having an outside option, and for the indirect reason of being able to reduce precautionary savings. That is, the harm is coming both from the first moment, the average shock to agricultural productivity, but also the second moment, its variance.

How much does this matter is practice? Brooks and Donovan worked with a NGO that physically builds bridges in remote areas. In Nicaragua, floods during the harvest season are common, isolating villages for days at a time when the riverbed along the path to market turns into a raging torrent. In this area, bridges are unnecessary when the riverbed is dry: the land is fairly flat, and the bridge barely reduces travel time when the riverbed isn’t flooded. These floods generally occur exactly during the growing season, after fertilizer is bought, but before crops are harvested, so the goods market in both inputs and outputs is essentially unaffected. And there is nice quasirandom variation: of 15 villages which the NGO selected as needing a bridge, 9 were ruled out after a visit by a technical advisor found the soil and topography unsuitable for the NGO’s relatively inexpensive bridge.

The authors survey villages the year before and the two years after the bridges are built, as well as surveying a subset of villagers with cell phones every two weeks in a particular year. Although N=15 seems worrying for power, the within-village differences in labor market behavior are sufficient that properly bootstrapped estimates can still infer interesting effects. And what do you find? Villages with bridges have many men shift from working in the village to outside in a given week, the percentage of women working outside nearly doubles with most of the women entering the labor force in order to work, the wages inside the village rise while wages outside the village do not, the use of fertilizer rises, village farm profits rise 76%, and the effect of all this is most pronounced on poorer households physically close to the bridge.

All this is exactly in line with the dynamic general equilibrium model sketched out above. If you assumed that bridges were just about market access for goods, you would have missed all of this. If you assumed the only benefit was additional wages outside the village, you would miss a full 1/3 of the benefit: the general equilibrium effect of shifting out workers who are particularly capable working outside the village causes wages to rise for the farm workers who remain at home. These particular bridges show an internal rate of return of nearly 20% even though they do nothing to improve market access for either inputs and outputs! And there are, of course, further utility benefits from reducing risk, even when that risk reduction does not show up in income through the channel of increased investment.

November 2017 working paper, currently R&R at Econometrica (RePEc IDEAS version. Both authors have a number of other really interesting drafts, of which I’ll mention two. Brooks, in a working paper with Joseph Kaposki and Yao Li, identify a really interesting harm of industrial clusters, but one that Adam Smith would have surely identified: they make collusion easier. Put all the firms in an industry in the same place, and establish regular opportunities for their managers to meet, and you wind up getting much less variance in markups than firms which are induced to locate in these clusters! Donovan, in a recent RED with my friend Chris Herrington, calibrates a model to explain why both college attendance and the relative cognitive ability of college grads rose during the 20th century. It’s not as simple as you might think: a decrease in costs, through student loans of otherwise, only affects marginal students, who are cognitively worse than the average existing college student. It turns out you also need a rising college premium and more precise signals of high schoolers’ academic abilities to get both patterns. Models doing work to extract insight from data – as always, this is the fundamental reason why economics is the queen of the social sciences.

Like this:

The past decade has seen interesting work in many fields of economics on the importance of misallocation for economic outcomes. Hsieh and Klenow’s famous 2009 paper suggested that misallocation of labor and capital in the developing world costs countries like China and India the equivalent of many years of growth. The same two authors have a new paper with Erik Hurst and Chad Jones suggesting that a substantial portion of the growth in the US since 1960 has been via better allocation of workers. In 1960, they note, 94 percent of doctors and lawyers were white men, versus 62 percent today, and we have no reason to believe the innate talent distribution in those fields had changed. Therefore, there were large numbers of women and minorities who would have been talented enough to work in these high-value fields in 1960, but due to misallocation (including in terms of who is educated) did not. Lucia Foster, John Haltiwanger and Chad Syverson have a famous paper in the AER on how to think about reallocation within industries, and the extent to which competition reallocates production from less efficient to more efficient producers; this is important because it is by now well-established that there is an enormous range of productivity within each industry, and hence potentially enormous efficiency gains from proper reallocation away from low-productivity producers.

The really intriguing misallocation question, though, is misallocation of workers across space. Some places are very productive, and others are not. Why don’t workers move? Part of the explanation, particularly in the past few decades, is that due to increasing land use regulation, local changes in total factor productivity increase housing costs, meaning that only high skilled workers gain much by mobility in response to shocks (see, e.g., Ganong and Shoag on the direct question of who benefits from moving, and Hornbeck and Moretti on the effects of productivity shocks on rents and incomes).

A second explanation is that people, quite naturally, value their community. They value their community both because they have friends and often family in the area, and also because they make investments in skills that are well-matched to where they live. For this reason, even if Town A is 10% more productive for the average blue-collar worker, a particular worker in Town B may be reluctant to move if it means giving up community connections or trying to relearn a different skill. This effect appears to be important particularly for people whose original community is low productivity: Deyrugina, Kawano and Levitt showed how those induced out of poor areas of New Orleans by Hurricane Katrina would up with higher wages than those whose neighborhoods were not flooded, and (the well-surnamed) Bryan, Chowdhury and Mobarak find large gains in income when they induce poor rural Bangladeshis to temporarily move to cities.

Today’s paper, by Nakamura et al, is interesting because it shows these beneficial effects of being forced out of one’s traditional community can hold even if the community is rich. The authors look at the impact of the 1973 volcanic eruption which destroyed a large portion of the main town, a large fishing village, on Iceland’s Westman Islands. Though the town had only 5200 residents, this actually makes it large by Icelandic standards: even today, there is only one town on the whole island which is both larger than that and located more than 45 minutes drive from the capital. Further, though the town is a fishing village, it was then and is now quite prosperous due to its harbor, a rarity in Southern Iceland. Residents whose houses were destroyed were compensated by the government, and could have either rebuilt on the island or moved away: those with destroyed houses wind up 15 percentage points more likely to move away than islanders whose houses remained intact.

So what happened? If you were a kid when your family moved away, the instrumental variables estimation suggests you got an average of 3.6 more years of schooling and mid-career earnings roughly 30,000 dollars higher than if you’d remained! Adults who left saw, if anything, a slight decrease in their lifetime earnings. Remember that the Westman Islands were and are wealthier than the rest of Iceland, so moving would really only benefit those whose dynasties had comparative advantage in fields other than fishing. In particular, parents with college educations were more likely to be move, conditional on their house being destroyed, than those without. So why did those parents need to be induced by the volcano to pack up? The authors suggest some inability to bargain as a household (the kids benefited, but not the adults), as well as uncertainty (naturally, whether moving would increase kids’ wages forty years later may have been unclear). From the perspective of a choice model, however, the outcome doesn’t seem unusual: parents, due to their community connections and occupational choice, would have considered moving very costly, even if they knew it was in their kid’s best long-term interests.

There is a lesson in the Iceland experience, as well as in the Katrina papers and other similar results: economic policy should focus on people, and not communities. Encouraging closer community ties, for instance, can make reallocation more difficult, and can therefore increase long-run poverty, by increasing the subjective cost of moving. When we ask how to handle long-run poverty in Appalachia, perhaps the answer is to provide assistance for groups who want to move, therefore gaining the benefit of reallocation across space while lessening the perceived cost of moving (my favorite example of clustered moves is that roughly 5% of the world’s Marshall Islanders now live in Springdale, Arkansas!). Likewise, limits on the movement of parolees across states can entrench poverty at precisely the time the parolee likely has the lowest moving costs.

Like this:

Why don’t people like renters? Looking for rental housing up here in Toronto (where under any reasonable set of parameters, there looks to be a serious housing bubble at the moment), it seems very rare for houses to be rented and also very rare for rental and owned homes to appear in the same neighborhood. Why might this be? Housing externalities is one answer: a single run-down house on the block greatly harms the value of surrounding houses. Social opprobrium among homeowners may be sufficient to induce them to internalize these externalities in a way that is not true of landlords. The very first “real” paper I helped with back at the Fed showed a huge impact of renovating run-down properties on neighborhood land values in Richmond, Virginia.

Given that housing externalities exist, we may worry about policies that distort the rent-buy decision. Rent control may not only limit incentives for landlords to upgrade the quality of their own property, but may also damage the value of neighboring properties. Autor, Palmer and Pathak investigate a quasiexperiment in Cambridge, MA (right next door to my birthplace of Boston, I used to hear Cambridge referred to as the PRC!). In 1994, Massachusetts held a referendum on banning rent control, which was enforced very strongly in Cambridge. It passed 51-49.

The units previously under rent control, no surprise, saw a big spurt of investment and a large increase in their value. If the rent controlled house was in a block with lots of other rent controlled houses, however, the price rose even more. That is, there was a substantial indirect impact where upgrades on neighboring houses increases the value of my previously rent-controlled house. Looking at houses that were never rent controlled, those close to previously rent-controlled units rose in price much faster than otherwise-similar houses in the same area which didn’t have rent-controlled units on the same block. Overall, Autor et al estimate that rent decontrol raised the value of Cambridge property by 2 billion, and that over 80 percent of this increase was due to indirect effects (aka housing externalities). No wonder people are so worried about a rental unit popping up in their neighborhood!

Like this:

Rebecca Diamond, on the market from Harvard, presented this interesting paper on inequality here on Friday. As is well-known, wage inequality increased enormously from the 1970s until today, with the divergence fairly well split between higher wages at top incomes and higher incomes to higher educated workers. There was simultaneously a great amount of locational sorting: the percentage of a city’s population which is college educated ranges from 15% in the Bakersfield MSA to around 45% in Boston, San Francisco and Washington, DC. Those cities that have attracted the highly educated have also seen huge increases in rent and housing prices. So perhaps the increase in wage inequality is overstated: these lawyers and high-flying tech employees are getting paid a ton, but also living in places where a 2,000 square foot house costs a million dollars.

Diamond notes that this logic is not complete. New York City has become much more expensive, yes, but it’s crime rate has gone way down, the streets are cleaner, the number of restaurants per capita has boomed, and the presence of highly educated neighbors and coworkers is good for your own productivity in the standard urban spillover models. It may be that wage inequality is underestimated using wage alone if better amenities in cities with lots of educated workers more than compensates for the higher rents.

How to sort this out? If you read this blog, you know the answer: data alone cannot tell you. What we need is a theory of high and low education workers’ location choice and a theory of wage determination. One such theory lets you do the following. First, find a way to identify exogenous changes in labor demand for some industry in cities, which ceteris parabis will increase the wages of workers employed in that industry. Second, note that workers can choose where to work, and that in equilibrium they must receive the same utility from all cities where they could be employed. Every city has a housing supply whose elasticity differs; cities with less land available for development because of water or mountains, and cities with stricter building regulations, have less elastic housing supply. Third, the amenities of a city are endogenous to who lives there; cities with more high education workers tend to have less crime, better symphonies, more restaurants, etc., which may be valued differently by high and low education workers.

Estimating the equilibrium distribution of high and low skill workers takes care. Using an idea from a 1991 paper by Bartik, Diamond notes that some shocks hit industries nationally. For instance, a shock may hit oil production, or hit the semiconductor industry. The first shock would increase low skill labor demand in Houston or Tulsa, and the second would increase high skill labor demand in San Jose and Boston. This tells us what happens to the labor demand curve. As always, to identify the intersection of demand and supply, we also need to identify changes in labor supply. Here, different housing supply elasticity helps us. A labor demand shock in a city with elastic housing supply will cause a lot of workers to move there (since rents won’t skyrocket), with fewer workers moving if housing supply is inelastic.

Estimating the full BLP-style model shows that, in fact, we are underestimating the change in well-being inequality between high and low education workers. The model suggests, no surprise, that both types of workers prefer higher wages, lower rents, and better amenities. However, the elasticity of college worker labor supply to amenities is much higher than that of less educated workers. This means that highly educated workers are more willing to accept lower after-rent wages for a city with better amenities than a less educated worker. Also, the only way to rationalize the city choices of highly educated workers over the time examined is with endogenous amenities; if well-being depends only on wages and rents, then highly educated workers would only have moved where they ended moving if they didn’t care at all about housing prices. Looking at smaller slices of the data, immigrant workers are much more sensitive to wages: they spend less of their income on housing, and hence care much more about wages when deciding where to live. In terms of spillovers, a 1% increase in the ratio of college educated workers to other workers increases college worker productivity by a half percentage point, and less educated worker productivity by about .2 percentage points.

Backing out the implies value of amenity in each MSA, the MSAs with the best amenities for both high and low education workers include places like Los Angeles and Boston; the least desirable for both types include high-crime Rust Belt cities. Inferred productivity by worker type is very different, however. While both types of workers appear to agree on which cities have the best and worst amenities, the productivity of high skill workers is highest in places like San Jose, San Francisco and New York, whereas productivity for low skill workers is particularly high in San Bernardino, Detroit and Las Vegas. The differential changes in productivity across cities led to re-sorting of different types of workers, which led to differential changes in amenities across cities. The observed pattern of location choices by different types of workers is consistent with a greater increase in well-being between high and low education workers, even taking into account changes in housing costs, than that implied by wage alone!

The data requirements and econometric skill involved in this model is considerable, but it should allow a lot of other interesting questions in urban policy to be answered. I asked Rebecca whether she looked at the welfare impacts of housing supply restrictions. Many cities that have experienced shocks to high education labor demand are also cities with very restrictive housing policies: LA, San Francisco, Boston, DC. In the counterfactual world where DC allowed higher density building, with the same labor demand shocks we actually observed, what would have happened to wages? Or inequality? She told me she is working on a similar idea, but that the welfare impacts are actually nontrivial. More elastic housing supply will cause more workers to move to high productivity cities, which is good. On the other hand, there are spillovers: housing supply restrictions form a fence that makes a city undesirable to low education workers, and all types of workers appear to both prefer highly educated workers and the amenities they bring. Weighing the differential impact of these two effects is an interesting next step.

November 2012 working paper (No IDEAS version). Fittingly on the week James Buchanan died, Diamond also has an interesting paper on rent extraction by government workers on her website. Roughly, government workers like to pay themselves higher salaries. If they raise taxes, private sector workers move away. But when some workers move out, the remaining population gets higher wages and pays lower rents as long as labor demand slopes down and housing supply slopes up. If housing supply is very inelastic, then higher taxes lead to workers leaving lead to a large decrease in housing costs, which stops the outflow of migration. So if extractive governments are trading off higher taxes against a lower population after the increase, they will ceteris parabis have higher taxes when housing supply is less elastic. And indeed this is true in the data. Interesting!

Like this:

Are certain cities nice places to live, “consumer cities” in the words of Ed Glaeser, or are they Dickensian, crime-filled hubs useful solely for their aggregation externalities on the production side? Economists have, no surprise, tried to solve that debate. On the one hand, if cities were so great, then why are wages so high, even for unskilled labor? Surely this is a sign that workers are being paid extra to cover the disutility they face from living in a city. On the other hand, if cities were so awful, then why is residential land so expensive? Surely, workers are only willing to pay a premium for an identical house if they enjoy its location.

Economics helps sort out the puzzle. Assume workers can take a job in any city they wish. In equilibrium, their utility, including the utility they receive from non-market quality of life factors, must equate across cities. A place with high quality of life must have relatively high local costs (such as housing) relative to the local wage, once we adjust for demographic and industry characteristics, or more workers would move there. This exercise has been done for decades now, and the results are puzzling: a 2003 estimate of quality of life by states had Wyoming and South Dakota in first and second place. This doesn’t appear to match our intuition. Further, large cities (in the sense of urban areas, not in the sense of core cities) are very strongly associated with low quality of life.

David Albouy has reestimated quality of life in places across the United States, with a few adjustments. First, he adjusts wages for the progressivity of the tax code. High-wage cities are, because of higher marginal tax rates, not so much richer than low-wage cities, hence the unobserved quality of life component of utility in high-wage cities is higher than in an estimate without this adjustment. Second, he notes that the share of spending on local goods (like housing) is higher than that used in previous estimates of quality of life. This means that previous estimates of quality of life are biased against cities with high cost-of-living. Third, he accounts for labor wages per household in a slightly more sophisticated way.

These changes make a big difference. The cities with the highest quality of life, in labor market equilibrium, roughly match your intuition: Honolulu, Santa Barbara, Monterey, San Francisco, San Luis Obispo, Santa Fe, non-metro Hawaii, Cape Cod, San Diego and rural Colorado are highest, while Decatur, IL, Beaumont, TX and Kokomo, IN bring up the rear. Hawaii, California, Vermont and Colorado have the highest QOL among states, while West Virginia, Mississippi, Michigan and Texas have the least. Most of the difference in the quality of life measure is predicted solely by measures of temperate weather, sun, coastal proximity and hilly topography. Large and dense cities, overall, have no lower quality of life than small cities or rural areas; that is, cities are no longer Dickensian, and their cultural amenities balance their congestion-based disamenities. Adjusting these figures for moving costs or heterogeneity makes the above even starker, as both adjustments imply that faster growing cities are, all else equal, higher quality of life than slower growing cities, the intuition being that with heterogeneous tastes for quality-of-life amenities, the next potential migrant values the amenities less than the migrants who have already arrived, hence wages must be higher in faster growing cities with the same quality of life as slower growing cities.

It’s not totally clear to me how we use the results of this estimation, though knowledge for its own sake is certainly worthwhile. It is a great example, though, of how we can use a bit of economic theory to answer seemingly impossible questions like “how valuable is it to live by the sea?” Answers that don’t account for labor market general equilibrium effects, or that rely on survey responses, are far less interesting to me than the type of answer Albouy provides. My strong hunch is that clever empiricists with a good bit of theoretical training can tell us much more about the value of non-market goods that we currently understand.

May 2012 Working Paper (IDEAS version). Albouy notes on his site that this is currently under review for the JPE. (I, and perhaps I alone, find it interesting how “typecast” the major journals in economics are. If you told me this particular paper was potentially coming out at a top journal and asked me to guess, I would tell you “JPE” without a second’s thought. Shall we say that Econometrica is the high theory, AER is diverse but with a recent heavy bias toward experimental in all its guises, ReStud is my favorite kind of old-school applied theory, JPE are articles that the New York Times might write about, and QJE tilts very much toward the empirical style popular at Harvard and MIT?)

Like this:

Since the Bayh-Dole Act of 1980, universities that receive federal research funding have been encourage to patent their research and license it to private industry. The benefits of the patenting rule are still very much in dispute, but surely everyone can agree that the spread of academic research into real firms has been a net positive? At many universities, faculty are even explicitly encouraged to commercialize their research, through start-ups and other means.

Astebro et al point out that something is missing here, though. Google is a university spinoff, but spun off by two PhD students at Stanford. Facebook and Microsoft are university spinoffs, but spun off by undergraduate students. Indeed, faculty don’t seem to be very entrepreneurial at all: the median top-100 US university in any given year has zero such spinoffs.

Using data from a (somewhat) longitudinal survey of university undergraduates, along with existing faculty spinoff data, the authors point out that students, within a few years of graduating, are twice as likely as faculty during that period to form their own business. Since there are so many more students, this means that 24 times more startups come from recent university grads than from faculty. And these are not low-quality startups or disguised unemployment: 36 percent of the businesses are still around in a follow-up survey two years later. Earnings from these startups is particularly strong for those students who claim their business is in an area related to their studies, and for students at research universities in the NRC top 10 for doctoral research.

There is very little in the way of identification in this paper, so read this as only a first go through the data. Nonetheless, the basic point is clear: if you are a region who wants to leverage universities for new business growth, developing better and more entrepreneurial students seems to trump encouraging faculty to run businesses. Indeed, to the extent that faculty create the human capital these students will use in their businesses, such faculty-biased policies may be counterproductive. The authors discuss a couple case studies, including the interesting E-school program at Chalmers in Gothenberg, Sweden, which perhaps provide a way forward here.

A broader takeaway, which hopefully is well-known already: the link between urban policy and invention/entrepreneurship policy is ridiculously important.

Like this:

There is something of a paradox when it comes to invention, particularly before the 18th century in what might be termed The Malthusian Era. We had many prominent and apparently massively influential inventions like writing, bronze, the wheel, agriculture and the gun, yet historians find little evidence of these inventions on incomes in the areas where they were invented. What might explain this? Do we simply not have good enough data to notice an effect? Is every bit of growth being swallowed up by a larger population, as in the standard Malthus explanation?

Jeremiah Dittmar considers the case of the Gutenberg press. Although printed works represented a very small segment of the 15th and 16th economy, and therefore the direct economic effects of the press are surely too small to be noticed, the indirect effects are often thought to be large. Merchants’ tables and mathematical techniques disseminated widely, literacy increased as Bibles spread in the protestant world, and atlases such as those of Blaeu were made available. Dittmar reexamines the case for economic effects of the printing press by looking at growth in city population – a good proxy for economic growth in the 1500s – in cities that had the press versus those that did not. He finds cities that got the press grew quite a bit faster than those did not. The city population data is nice in that it uses urban agglomeration data, rather than administrative boundary data; would that all urban economics handled this point properly!

There is obvious endogeneity, of course. Those cities that are booming would import a press first. To handle this issue, Dittmar runs an IV regression using distance from Mainz, Germany as an instrument. The press was invented in Mainz, and the techniques needed to build it were a secret known only to a guild in that city. For this reason, the press disseminates more or less in concentric circles from Mainz over time, but nonetheless there is some variability. The effect of press adoption before 1500 on city population growth from 1500-1600 is positive and significant. If an alternative instrument is used – say, distance from Amsterdam or Wittenberg – the effect of the press is no longer seen. Also, the cities that adopted the press, in the IV regression, grew no faster than cities that did not in the pre-1450 century.

That’s not a bad little piece of economic history. Actually, Dittmar appears to have a handful of other interesting looking urban history working papers on his website. One suggests that Zipf’s Law did not hold until the era of modern economic growth began. A second uses divergence from power law distribution of cities to identify heavily-distorted economies in the modern world, which is both a clever idea and one I wish I’d thought of first!

Like this:

“Urban renewal” has been a dominant theme of the past 60 years, and not only in the US. The basic idea is that housing values depend on the value of nearby houses, in that poorly-kept houses lower nearby house values, and well-groomed lawns do the opposite. That is, the quality of housing stock has a large externality component. Of course, the magnitude of this effect is very difficult to measure. If I renovate my home, and you – the analyst – see house prices increase in my neighborhood, there is a ton of endogeneity; perhaps I renovated because there was some secular gentrifying trend within my neighborhood already present.

The authors of this recent JPE use a unique dataset to get around this problem. In the 1990s, the city of Richmond, Virginia gave grant money to CDCs to renovate housing stock in four poor neighborhoods in that city; millions of dollars in each neighborhood were given. Of note, a fifth neighborhood was considered and left out of the program because it was in the same city council district as a neighborhood already chosen. Using over 100,000 residential house sales, the authors strip housing prices down to land value using a semiparametric technique, then investigate what happens to land prices in the city after the program ends. The result is striking: home prices very close the the center of CDC work increases 6-10% faster per year than elsewhere in the city, the effect was noticeable at a distance of half a mile. Discounting future tax revenue, the housing externalities in run-down neighborhoods near the city center were so strong as to almost pay for the renovation themselves.

(I particularly like this paper because I spent a good chunk of time when I was at the Richmond Fed working with this data. The takeaway for my own future work is that nonparametric estimation, when done smartly, can be used even with very large datasets, and that urban data (land value, crime rates, etc.) are far too “lumpy” to be captured with the simple econometric techniques often deployed in urban econ. A paper with one of the authors of this JPE that was published in a Fed journal a few years ago shows this quite plainly when it comes to land values in a metro area.

Like this:

It is commonly noted that there is an urban wage premium, perhaps because of agglomeration effects on productivity, or to compensate (in utility terms) for higher traffic and land costs. S. Lee presents evidence that, at least in some fields, there is a negative urban wage premium for high skill workers. The story is thus: high skill workers have greater taste for variety in consumption, and cities provide such variety; if the extra utility from consumption variety grows faster than rents, and utility is equal in rural and urban areas, then urban areas could show lower wages for high skill workers. Since high skill workers are relatively cheap in cities, they will be used in firms in higher proportion than low skill workers. Data on the medical profession bears this out: hospitals use a higher ratio of doctors to nurses in cities, the doctors are paid less in real terms in cities, and the nurses are paid more in real terms in cities than in rural areas. Further, these urban doctors tend to work in higher skill specialties, and be graduates of higher ranked medical schools, than higher paid rural doctors. This is yet more evidence of the consumer-driven rationale for cities: production is not everything.

I should note, however, that the data in the paper violates my major urban econ pet peeve by using MSA level data. Look at a map of MSAs in the US: they bear very little resemblance to what we might call “cities”, and it’s tough to argue that a worker 50 miles from the city center is somehow taking lower wages in exchange for urban amenities. I understand that a lot of data is given at the MSA level, but we really should be focusing on tighter definitions; my preference is the Census “urbanized area”.

Like this:

In this recent lecture to the Association of American Geographers, Krugman offers a brief defense of a mathematical model investigation of geography (as opposed to the “discursive” atheoretical style in vogue among non-economist geographers today), and counters the criticism that New Economic Geography is irrelevant to the world today. While NEG generates its main results through transport costs, and does a good job of explaining industrial clusters, the US and Europe do not seem to be defined by industrial clusters as much today as in the past (“Pittsburgh was a steel city, Atlanta today is a…what?”). To explain modern cities, information seems to be the critical element (in addition to consumer cities a la Glaeser). Nonetheless, traditional NEG does do a good job explaining the developing world, such as China; this is unsurprising, since economically these countries look a lot like the US in 1900, which NEG was basically invented to explain.